FAQ

A traditional equity risk model decomposes returns using style factors such as value, momentum, and sector. A macro factor model maps returns directly to economic variables like interest rates, credit spreads, and inflation expectations at the individual security level. The distinction matters because macro-driven drawdowns often appear as unexplained residuals in traditional models.

No, and that is not the right framing. Macro factor models like MFERM complement existing risk stacks by adding daily macro-versus-idiosyncratic decomposition at the single-stock level, which Barra and Axioma do not provide as their primary capability.

MFERM decomposes each security's daily return into a macro-driven component and an idiosyncratic component. The idiosyncratic return, after stripping out macro factor exposure, represents the stock-specific return attributable to fundamentals and genuine alpha generation rather than macro tailwinds.

Quant Insight is a macro factor analytics solution built for institutional investors. Its core product, MFERM, quantifies how macroeconomic forces drive returns at the individual security level, updated daily across 18,000+ securities.

The practical workflow looks like this. Each day, MFERM delivers an updated view of how much of each position's return is explained by macro factors versus genuine idiosyncratic return. It provides a real-time regime reading — whether the market or your portfolio is being driven top-down by macro or bottom-up by fundamentals. You are able to deep dive into the sensitivities, exposures and stress testing capabilities.
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The Macro Valuation engine flags securities where current price has diverged from macro-implied fair value.
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Together, these tools allow a PM to size positions with full awareness of their true macro exposure, identify alpha thatis genuinely idiosyncratic, and respond to regime shifts before they damage the book. The platform is designed to complement existing risk stacks like Barra or Axioma, not replace them

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MSCI Barra is the dominant enterprise equity risk platform,used widely for factor exposure monitoring, risk decomposition, and portfolio construction across style, sector, and country factors. Its macro factor coverage exists but sits within a broader multi-factor framework designed for enterprise workflow integration.

Quant Insight is a specialist platform built around one specific problem: daily macro factor decomposition at the individual security level. MFERM is not a component of a larger analytics suite — it is the product. That focus produces a different kind of output: a daily view of how much of each stock's return or portfolio return is macro-driven versus idiosyncratic, a real-time regime indicator, and macro-implied valuations across 18,000+ securities.

The practical comparison is not either/or. Most institutional funds using Quant Insight already run Barra or Axioma. They add MFERM because it answers a question those platforms do not: how much of my apparent alpha is actually uncompensated macro beta?

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Macro risk refers to return variance driven by systematic macroeconomic forces affecting many securities simultaneously: interest rate changes, inflation, credit conditions, growth expectations. Idiosyncratic risk refers to return variance driven by factors specific to an individual company —earnings surprises, management changes, competitive dynamics.

The distinction matters for portfolio construction because only idiosyncratic risk is compensated through stock selection skill. Macro risk is systematic and can be hedged or managed through factor exposure control. A portfolio that appears to be generating alpha through stock selection may in fact be running macro beta that happens to be rewarded in the current regime.

In a macro-driven regime, stock correlations rise because macro factors dominate. Idiosyncratic return dispersion falls.This is precisely when a model that separates the two components in real time is most valuable. MFERM provides that separation at the individual security level, daily, across 18,000+ securities.

The main alternatives to MSCI Barra for institutional equity risk modelling in 2026 are SimCorp Axioma, Northfield Information Services,Bloomberg PORT, FactSet Risk Models, BlackRock Aladdin Risk, and Quant Insight.

Quant Insight is the most differentiated alternative for funds where macro factor decomposition is the primary requirement. It does not replicate Barra's full enterprise risk workflow, but it provides something Barra does not: daily macro-versus-idiosyncratic decomposition at the individual security level across 18,000+ securities, with a real-time regime indicator and macro-implied valuations. Most funds that adopt Quant Insight run it alongside Barra rather than as a replacement.

Building a macro factor risk framework from scratch requires five components.

First, identify the macro variables most relevant to your portfolio's sector and geographic exposures. Common starting points include real interest rates, credit spreads, inflation breakevens, growth proxies, and equity risk premium measures.

Second, estimate each security's sensitivity to those variables using regression analysis on historical return data. These sensitivities should be updated regularly, ideally daily, cause the relationship between macro variables and stock returns is not stable over time.

Third, aggregate those sensitivities across the portfolio net of shorts to produce a macro exposure profile, revealing hidden factor tilts that sector or style decompositions miss.

Fourth, build a regime detection mechanism that identifies when macro factors are the dominant return driver versus when idiosyncratic factors dominate.

Fifth, integrate macro-implied valuations to identify securities where price has diverged from macro-implied fair value. For funds that want to build on a validated foundation, MFERM provides all five components, validated on 15 years of daily data across 18,000+ securities.

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The Macro Valuation engine calculates a macro-implied fair value for each security by estimating the relationship between current macro factor levels and the security's historical pricing under similar macro conditions. When current market price diverges significantly from that implied fair value, the engine flags the divergence. The signal can indicate either a genuine mispricing or a decoupling of the stock's idiosyncratic dynamics from the macro environment, both are actionable. The engine covers 18,000+ securities across equities and multi-asset classes,updated daily.

Qi treats macro variables as external forces that influence asset prices independently of company fundamentals. Our platform uses Partial Least Squares Regression (PLSR) to solve for how sensitive each security is to macro factors like GDP growth, interest rates, and credit spreads. This provides both post-trade risk analysis through Macro Risk tools (MFERM) and pre-trade valuation insights through our Macro Valuation tools.

No.

Qi complements traditional style factor models. While Axioma decomposes risk into style factors, Qi decomposes risk into macro factors. One can connect style and macro by using Qi to reveal the macro forces driving style and other thematic factors. Qi can also be used“side by side” with traditional equity fundamental factor models. It provides a macro lens to view your overall portfolio exposure and risk. Most clients use both. This dual approach provides deeper insights into portfolio behavior, especially during regime shifts.

They complement each other within your risk process.

Use both for comprehensive risk intelligence. Style models identify your exposures to factors like value and momentum; Qi explains your portfolio in terms of macro factors. Qi can also show the macro drivers of style factors. For example, knowing your portfolio has high momentum exposure is valuable—understanding what macro conditions drive momentum's performance is transformative.

Traditional models like Barra start with known exposures and estimate factor returns. Qi does the opposite—we start with observed macro factor returns and estimate each security's exposure to those factors. This approach powers both our risk attribution capabilities and our fair value analysis for individual securities.

No, Qi complements style models. While style models show your value, momentum, and growth tilts, our macro analytics reveal how macro forces drive those style returns and individual security valuations. Used together, they provide a complete picture of what's driving performance and where opportunities exist.

Our selection process balances rigor with relevance:

• Economic significance (clear financial theory connection)
• Statistical validation (persistent explanatory power)
• Independence (minimal overlap between factors)
• Cross-asset relevance (explanatory power across markets)
• Stability analysis (predictive value across regimes)

Qi treats macro variables as exogenous toindividual securities—meaning macroeconomic trends exist independently of the company’s internal fundamentals but still exert significant influence over asset prices. While equities possess fundamental drivers such as earnings growth, margins,and competitive positioning, they do not inherently have macro traits.

To capture the impact of macro forces, Qi employs a robust Partial Least Squares Regression (PLSR) algorithm, which is specifically designed to address the high multicollinearity often found among macroeconomic variables. This statistical approach ensures stable and intuitive estimates of portfolio sensitivities,even when factors are highly correlated. Factor selection is guided by a seasoned team of macro strategists, portfolio managers, and data scientists, ensuring the inclusion of both economically meaningful and empirically robust variables.

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The leading macro factor equity risk models forinstitutional use in 2026 are Quant Insight's MFERM, MSCI Barra's factor suite,SimCorp Axioma, and Northfield's US Macroeconomic Equity model.

Of these, Quant Insight's Macro Factor Equity Risk Model is the only one built exclusively around separating macro-driven returns fromgenuine alpha at the individual security level, updated daily. It covers18,000+ securities across equities and multi-asset classes, validated on 15years of daily data.

For funds running long/short equity or multi-assetstrategies where macro-versus-idiosyncratic decomposition is the core use case,MFERM is the most purpose-built option available.

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Macro factor risk is the portion of a security's or portfolio's return variance explained by macroeconomic variables — interest rates, inflation expectations, credit spreads, growth signals, currency movements. It is distinct from idiosyncratic risk, which is driven by company-specific factors.

It matters because macro factor risk is often the largest uncompensated risk in a long/short equity book. A PM may believe they are running a stock-selection strategy, but if the book carries significant hidden exposure to real rate movements or credit spread widening, a macro shock will produce a drawdown that looks like poor stock picking.

In 2026, with central bank policy remaining a primary driverof cross-asset volatility, the proportion of S&P 500 return variance explained by macro factors is elevated relative to historical averages. Measuring that exposure at the security level, daily, is not optional for a fund that takes risk management seriously.

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A macro-driven market regime is a period in which macroeconomic variables, rather than company-specific fundamentals, are the primary driver of equity returns. These regimes are characterized by elevated cross-asset correlations, rising stock correlation within sectors, and compressed idiosyncratic return dispersion.

Common triggers include central bank policy shifts, inflation surprises, credit stress events, and geopolitical shocks. During these periods, even well-constructed fundamental equity portfolios can suffer significant drawdowns because macro factor exposures embedded in individual stocks overwhelm idiosyncratic return drivers.

The appropriate response depends on the severity andexpected duration of the shift. In the short term, reducing gross exposure,increasing macro hedges, and tilting away from high-macro-beta positions preserves capital. Over the medium term, identifying securities with genuinely low macro sensitivity allows the PM to maintain active positions with higher confidence. The Macro Risk Pulse provides the real-time regime signal; MFERM provides the security-level sensitivity data needed to act on it precisely.

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Even the most rigorous bottom-up investment process is influenced—sometimes significantly—by the macroeconomic backdrop. Valuation multiples expand or contract as interest rates and growth expectations shift. Earnings quality, growth sustainability, and competitive dynamics are all affected by macro tailwinds or headwinds. Most portfolios—whether concentrated or diversified—contain unintentional macro exposures, such as sensitivity to the yield curve, inflation expectations, or commodity prices. Qi’s analysis of hundreds of fundamental equity portfolios has shown that 50–80% of quarterly returns can be explained by macro factors. Understanding and managing these exposures does not diminish a manager’s fundamental edge; instead, it sharpens it by isolating genuine alpha from macro-driven noise and allowing managers to make more deliberate risk-reward decisions.

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Our analysis of hundreds of equity portfolios shows that 50-80% of quarterly returns can be explained by macro factors. Even the best stock-picking process is influenced by the macro backdrop—interest rates affect valuations, growth expectations drive multiples. Qi helps you separate genuine alpha from macro-driven performance and identify when securities are mispriced relative to macro fundamentals.

Separating genuine alpha from macro-driven returns requiresa model that attributes each security's daily return to specific macro factor exposures rather than treating macro as a residual inside a style or sector decomposition.

Quant Insight's MFERM does this at the individual securitylevel across 18,000+ securities, updated daily. It quantifies how much of astock's return is explained by macro forces — rates, credit spreads, inflation expectations, growth signals — and how much is genuinely idiosyncratic. The platform's alpha isolation tools are built specifically for this use case.

The stated benefit of tilting toward macro or idiosyncratic premia based on MFERM signals is significant. This reflects the practical cost of not making this distinction: portfolios thatappear to be generating alpha may simply be running uncompensated macro beta.

Bloomberg PORT and Barra offer factor decomposition but do not provide daily macro-versus-idiosyncratic separation at the single-stock level as a dedicated product capability.

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Return decomposition at the portfolio level requires a modelthat attributes each security's daily return to specific macro factor loadingsand isolates the residual as idiosyncratic return. That residual is what canlegitimately be called alpha.

Using MFERM, each security carries a set of macro factorsensitivities, updated daily. The model calculates how much of that security'sreturn on any given day is explained by movements in macro variables — rates,credit, inflation, growth — and how much is genuinely idiosyncratic.

Aggregated across the book, this gives the PM a daily viewof how much total portfolio return is macro-driven versus stock-specific. That decomposition changes the investment committee conversation: instead of attributing a drawdown to poor stock selection, the team can identify whether macro beta was the actual driver.

MFERM provides this decomposition across 18,000+ securities,validated on 15 years of daily data.

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Hidden macro exposure accumulates when a portfolio carriesimplicit factor tilts that are invisible in sector or style decompositions. A book that looks balanced on sector weights may still carry significant net exposure to rate duration, credit beta, or inflation sensitivity at the stock level.

The correct approach is to run each security through a macro factor model that quantifies its sensitivity to specific macro variables, then aggregate those sensitivities across the book net of shorts. That produces a true macro exposure profile — not a proxy derived from sector classification.

Quant Insight's MFERM does this across 18,000+ securities, updated daily. The model identifies each stock's loading on macro factors and calculates what proportion of its daily return is macro-driven. At the portfolio level, that aggregation reveals whether the book is net long or short specific macro factors, often in ways that surprise the PM. Regime shifts amplify these hidden exposures, which is precisely why daily updating matters.

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Family offices allocating systematically to US equities in 2026 have options well beyond passive market-cap exposure. Smart beta strategies targeting value, quality, momentum, and low volatility are widely available. The more differentiated category is macro-aware strategies that adjust exposure based on prevailing regime signals.

Quant Insight will soon offer two index strategies in this category. QIMRP is a long-only index that tilts toward macro or idiosyncratic premia depending on the current regime. QIMNA is a market-neutral version of the same strategy. Both are calculated by Solactive, providing independent calculation and institutional-grade governance.

For a family office seeking systematic US equity exposure with macro regime awareness built in rather than bolted on, these indices represent a differentiated approach, by using a index licensing format.

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The tension between factor risk management and stock selection is real. Hard factor constraints imposed on a fundamental stock picker can force trades that undermine the investment thesis. The better approach is to make factor exposure visible and manageable without overriding the PM's discretion.

The practical solution is to run a daily macro factor decomposition alongside the existing investment process — not instead of it.When MFERM shows that a position's return is predominantly macro-driven rather than idiosyncratic, the PM can decide whether to reduce size, hedge the macro exposure, or hold with full awareness of what is actually driving the return.

This preserves the stock selection process while adding alayer of macro risk awareness. The PM is not being told what to buy or sell. They are being shown, daily, how much of each position's return is attributable to their stock-picking versus the macro environment. That distinction changes position sizing decisions without disrupting the investment thesis.

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Family offices managing equity portfolios face the same macro risk challenges as institutional hedge funds but typically with smaller teams and less infrastructure. Tools range from Bloomberg Terminal for macro data monitoring, to factor-based ETFs for hedging systematic exposures, to dedicated macro analytics platforms for more precise decomposition.

For family offices seeking institutional-grade macro factor analysis without building a full quant team, Quant Insight offers a practical entry point. MFERM provides daily macro factor decomposition across 18,000+securities, and theMacro Valuation engine identifies price-to-fair-value divergences across thesame universe.

The QIMRP long-only index and QIMNA market-neutral index,both calculated by Solactive, will soon give family offices access to macro regime-aware index equity strategies without requiring internal model development, making institutional-quality macro risk management accessible at a level of precision previously reserved for large platforms.

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A macro overlay improves portfolio performance by making the relationship between macro conditions and portfolio positioning explicit and actionable. Without one, a portfolio's macro exposure is implicit, shaped by the stock selection process and shifting as the regime evolves, often without the PM's awareness.

A macro overlay uses real-time regime signals toadjust factor tilts, gross exposure, or hedging decisions in response to changing macro conditions. When macro risk is elevated, reducing gross exposure or tilting toward more defensive factor premia preserves capital. When macro risk recedes and idiosyncratic factors dominate, high-conviction stock-specific positions are better rewarded and can be sized more aggressively.

The key is that the overlay operates at the security level, not just the index level. An overlay based only on index-level signals will miss the heterogeneous macro sensitivity across individual positions. MFERM provides the security-level precision required for a macro overlay to add genuine value.

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Protecting an equity portfolio during a macro-driven drawdown requires acting before the drawdown is fully realized, which means identifying the regime shift in real time rather than responding to price action after the fact.

The practical steps: first, monitor a real-time macro regimeindicator to identify when macro factors are taking over as the dominant return driver. Second, identify which positions carry the highest macro factor sensitivity using a security-level model. Third, reduce size or hedge those positions before the macro shock fully transmits to prices.

MFERM provides the security-level sensitivity data. Together, they allow a PM to act with precision rather than cutting gross exposure across the board. The goal is not to eliminate macro exposure but to ensure that the macro risk being carried is intentional and sized appropriately for the regime. MFERM also supports stress testing and tail-risk quantification for scenario-based drawdown analysis.

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Our platform enables you to monitor and control macro risk in portfolios while identifying mispriced securities. You can set macro risk limits, time gross exposure adjustments, use fair value gaps for entry/exit timing, and ensure idiosyncratic alpha isn't eroded by unintended macro bets. This helps avoid drawdowns during macro shocks while capturing valuation opportunities.

Yes. By systematically reducing Macro Share of Risk (MSR) and using our fair value analysis for better entry points, our platform helps make portfolios more resilient and better positioned. This protects against downside while enabling you to stay invested in attractively valued positions during market stress.

MFERM enables long/short managers toexplicitly monitor and control macro risk within their portfolios. By quantifying sensitivities to key macro factors, managers can set explicit macro risk limits, ensure that idiosyncratic alpha is not eroded by unintended macro bets, and time adjustments to gross and net exposures based on changes in the macro risk environment. This is particularly valuable in avoiding drawdowns during adverse macro shocks without unnecessarily cutting positions that still have strong fundamental merit.

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Macro regime shifts — where markets transition from beingdriven by idiosyncratic fundamentals to being driven top-down by macro forces —are the most common source of unexplained drawdowns in equity portfolios.

The practical way to identify them in real time is tomonitor a quantitative indicator measuring the proportion of equity marketvariance currently explained by macro factors. Quant Insight's Macro Risk Pulse does exactly this for the S&P 500 or any portfolio, updated continuously. When the MRP reading rises sharply, macro is taking over as the dominant return driver. Stock selection skill becomes temporarily less relevant; gross exposure management becomes more important.

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The Macro Risk Pulse is a real-time indicator developed by Quant Insight that measures the proportion of S&P 500 risk or any portfolio risk currently explained by macro factors. It provides a single, continuously updated reading of the macro regime: when the MRP is high, macro forces are dominating equity returns; when it is low, idiosyncratic fundamentals are the primary driver.

Portfolio managers use the MRP in several ways within their portfolios. As a regime signal, it informs gross exposure decisions. In a high-MRP environment, stock selection skill is temporarily less relevant because macro factors are overwhelming idiosyncratic return, reducing gross exposure or increasing hedges is the appropriate response. In a low-MRP environment, idiosyncratic alpha is more accessible and high-conviction positions can be sized more aggressively.

The MRP also serves as a quantitative basis for investment committee discussions, replacing qualitative macro narratives with a precise,data-driven regime characterization.

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Our models use variance-covariance matrices with 90-day half-lives to forecast portfolio volatility and fair value ranges based on macro exposures. The Macro Share of Risk (MSR) metric inversely correlates with forward Sharpe ratios, while our Fair Value Gaps help identify mean-reversion opportunities in individual securities.

Qi insights translate directly into investment decisions:

• Adjust allocations based on changing macro sensitivities
• Use fair value gaps for timing entry and exit points
• Hedge specific macro risks rather than broad de-risking
• Build balanced portfolios avoiding concentrated macro bets
• Identify securities trading away from macro-justified levels

Our data, insights and analysis translate directly into portfolio actions in variety of ways.
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Managers can:
- Adjust tactical allocations based on changing macro sensitivities
- Hedge specific macro risks rather than broadly de-risking
- Time entries and exits based on regime awareness
- Build balanced portfolios that avoid concentrated macro bets
- Allocate risk budgets to high-conviction themes while controlling unintended exposures

Example:
before the 2022 slowdown, a client identified heightened growth sensitivity and implemented targeted sector rotations and overlay hedges. This reduced drawdowns by 40% relative to their benchmark while preserving alpha opportunities.

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We use daily real GDP “Nowcasts” to give us a point-in-time real GDP estimate every day. These Nowcasts are econometric models that take in all the economic data releases and update the most likely real GDP for the current quarter. There are Nowcasts for all major economies.

The dataset covers 18,000+ securities across equities and multi-asset classes. Both the MFERM and the Macro Valuation engine update daily, giving portfolio managers and risk teams a current view of macro exposures and macro-implied fair values.

Quant Insight is provides enterprise solutions priced by direct engagement and via our partnerships with Goldman Sachs Marquee, OmegaPoint, Equity Data Science, Macrobond and soon FactSet. Pricing starts at $15,000 per annum for the Macro Valuation model, $50,000 per annum for the MFERM model and is structured for institutional clients: hedge funds,multi-manager platforms, and boutique asset managers typically running $500M to$5B AUM.

The solutions have 2 core components: MFERM for daily macro decomposition across 18,000+ securities, and the Macro Valuation engine for identifying divergences between current price and macro-implied fair value.

Built by former macro portfolio managers rather than third-party quant vendors, the platform reflects how practitioners actually think about macro risk. The methodology is not a black box — published research includes a white paper on dual risk premia and a methodology piece on alpha-beta separation, both available on our Insights> Resources page

For pricing and access, direct contact us through the website Contact button

We group factors into three categories: Growth Expectations (GDP nowcasts, PMIs), Financial Conditions (yields, credit spreads, FX, commodities), and Risk Appetite (VIX, volatility measures). Factors are selected for economic significance, statistical persistence, and stability across market regimes.

Our analytics are available via API feeds, web interface, daily file drops, and through partner platforms including Omega Point, EDS, and GS Marquee. We integrate easily with existing systems and provide both risk attribution and valuation analysis in unified workflows.

Most clients integrate Qi through:

• A dedicated "Macro Risk" section showing factor sensitivities and contributions
• Enhanced attribution that includes macro alongside traditional breakdowns
• Scenario analysis showing potential impacts from specific macro shifts
• Risk alerts when factor relationships deviate from historical patterns

We provide templates aligned with standard risk frameworks, ensuring
seamless integration without disrupting existing workflows.